Patent AU-2020101288-A4 (AU)
Title: Method and System for Quantitatively Detecting Copper in Rice Leaves
Inventor: LIU FEI
IPC: G01N21/3586
Filed: 2020-07-08
Granted: 2020-08-13
Abstract: The present invention provides a method and system for quantitatively detecting copper (Cu) 
content in rice leaves. The method includes: obtaining rice leaf samples, laser-induced breakdown 
spectroscopy (LIBS) data and a true Cu content Y; establishing a simple linear regression (SLR) 
model of Cu emission line intensity and Cu content, and calculating a predicted Cu content Yi 
based on the model; establishing an exponential regression (ER) model of Cu emission line 
intensity and Cu content, and obtaining a maximum ReJ; calculating a predicted Cu content Yj 
based on the model; establishing a multiple linear regression (MLR) model of Cu emission line 
intensity and Cu content Yd, and calculating a predicted Cu content based on the model; and 
establishing a linear regression model of multi-equation combination predicted content and true 
Cu content, and finally determining a Cu content. The method of the present invention contains 
mathematical relationships such as linear, exponential and non-linear relationships between the 
LIBS spectra and the Cu content of the rice leaves to the maximum extent. This method retards 
matrix interference information and thus achieves accurate quantification of the Cu content.
1/2 
101 
Obtain rice leaf samples 
102 
Acquire laser-induced breakdown spectroscopy (LIBS) data of the rice leaf samples 
103 
Measure a true copper (Cu) content y in the rice leaf samples by using inductively 
coupled plasma mass spectrometry (ICP-MS) 
Obtain a characteristic band with a highest correlation with the true content from the 104 
spectral data by using a characteristic variable screening method 
105 
Quickly locate n Cu emission lines in the characteristic band 
Use a simple linear regression (SLR) method to establish n SLR models of Cu emission 106 
line intensity and Cu content of the test set samples, and obtain correlations RI, R2, ... , Rn 
between the true Cu content y and predicted contents yl, y2, ... , yn 
107 
Obtain a maximum correlation Ri among RI, R2, ... , Rn 
108 
Calculate a predicted Cu content yi based on an SLR model of Cu emission line 
intensity and Cu content corresponding to the maximum correlation Ri 
Use an exponential regression (ER) equation to establish n ER models of Cu emission 109 
line intensity and Cu content of the test set samples, and obtain correlations Rel, Re2, 
Ren between the true Cu content y and predicted contents yl, y2, ... , yn 
110 
Obtain a maximum correlation Rej among Re, Re2, ... , Ren 
111 
Calculate a predicted Cu content yj of an ER model of Cu emission line intensity and 
Cu content corresponding to the maximum correlation Rej 
Use a multiple linear regression (MLR) method to establish n MLR models of Cu 112 
emission line intensity and Cu content of the test set samples, and obtain a correlation Rd 
between the true Cu content y and a predicted content yd; 
113 
Calculate the predicted Cu content yd based on an MLR model of Cu emission line 
intensity and Cu content corresponding to the correlation Rd; 
Establish a linear regression model of test multi-equation combination predicted content 114 and true Cu content by using a linear regression method by taking Cu contents yi, yj and yd 
as an input vector and the true content y as an output vector 
115 
Determine a Cu content based on the linear regression model of multi-equation 
combination predicted content and true Cu content 
FIG. 1